Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opennlp.tools.ml.maxent.quasinewton;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import opennlp.tools.ml.AbstractEventTrainer;
import opennlp.tools.ml.ArrayMath;
import opennlp.tools.ml.maxent.quasinewton.QNMinimizer.Evaluator;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.Context;
import opennlp.tools.ml.model.DataIndexer;
import opennlp.tools.util.TrainingParameters;
/**
* Maxent model trainer using L-BFGS algorithm.
*/
public class QNTrainer extends AbstractEventTrainer {
public static final String MAXENT_QN_VALUE = "MAXENT_QN";
public static final String THREADS_PARAM = "Threads";
public static final int THREADS_DEFAULT = 1;
public static final String L1COST_PARAM = "L1Cost";
public static final double L1COST_DEFAULT = 0.1;
public static final String L2COST_PARAM = "L2Cost";
public static final double L2COST_DEFAULT = 0.1;
// Number of Hessian updates to store
public static final String M_PARAM = "NumOfUpdates";
public static final int M_DEFAULT = 15;
// Maximum number of function evaluations
public static final String MAX_FCT_EVAL_PARAM = "MaxFctEval";
public static final int MAX_FCT_EVAL_DEFAULT = 30000;
// Number of threads
private int threads;
// L1-regularization cost
private double l1Cost;
// L2-regularization cost
private double l2Cost;
// Settings for QNMinimizer
private int m;
private int maxFctEval;
public QNTrainer(TrainingParameters parameters) {
super(parameters);
}
// Constructor -- to log. For testing purpose
public QNTrainer(boolean printMessages) {
this(M_DEFAULT, printMessages);
}
// Constructor -- m : number of hessian updates to store. For testing purpose
public QNTrainer(int m) {
this(m, true);
}
// Constructor -- to log, number of hessian updates to store. For testing purpose
public QNTrainer(int m, boolean verbose) {
this(m, MAX_FCT_EVAL_DEFAULT, verbose);
}
// For testing purpose
public QNTrainer(int m, int maxFctEval, boolean printMessages) {
this.printMessages = printMessages;
this.m = m < 0 ? M_DEFAULT : m;
this.maxFctEval = maxFctEval < 0 ? MAX_FCT_EVAL_DEFAULT : maxFctEval;
this.threads = THREADS_DEFAULT;
this.l1Cost = L1COST_DEFAULT;
this.l2Cost = L2COST_DEFAULT;
}
// >> Members related to AbstractEventTrainer
public QNTrainer() {
}
@Override
public void init(TrainingParameters trainingParameters, Map reportMap) {
super.init(trainingParameters,reportMap);
this.m = trainingParameters.getIntParameter(M_PARAM, M_DEFAULT);
this.maxFctEval = trainingParameters.getIntParameter(MAX_FCT_EVAL_PARAM, MAX_FCT_EVAL_DEFAULT);
this.threads = trainingParameters.getIntParameter(THREADS_PARAM, THREADS_DEFAULT);
this.l1Cost = trainingParameters.getDoubleParameter(L1COST_PARAM, L1COST_DEFAULT);
this.l2Cost = trainingParameters.getDoubleParameter(L2COST_PARAM, L2COST_DEFAULT);
}
@Override
@Deprecated
public void init(Map trainParams, Map reportMap) {
init(new TrainingParameters(trainParams),reportMap);
}
@Override
public void validate() {
super.validate();
String algorithmName = getAlgorithm();
if (algorithmName != null && !(MAXENT_QN_VALUE.equals(algorithmName))) {
throw new IllegalArgumentException("algorithmName must be MAXENT_QN");
}
// Number of Hessian updates to remember
if (m < 0) {
throw new IllegalArgumentException(
"Number of Hessian updates to remember must be >= 0");
}
// Maximum number of function evaluations
if (maxFctEval < 0) {
throw new IllegalArgumentException(
"Maximum number of function evaluations must be >= 0");
}
// Number of threads must be >= 1
if (threads < 1) {
throw new IllegalArgumentException("Number of threads must be >= 1");
}
// Regularization costs must be >= 0
if (l1Cost < 0) {
throw new IllegalArgumentException("Regularization costs must be >= 0");
}
if (l2Cost < 0) {
throw new IllegalArgumentException("Regularization costs must be >= 0");
}
}
@Deprecated
@Override
public boolean isValid() {
try {
validate();
return true;
}
catch (IllegalArgumentException e) {
return false;
}
}
public boolean isSortAndMerge() {
return true;
}
public AbstractModel doTrain(DataIndexer indexer) throws IOException {
int iterations = getIterations();
return trainModel(iterations, indexer);
}
// << Members related to AbstractEventTrainer
public QNModel trainModel(int iterations, DataIndexer indexer) {
// Train model's parameters
Function objectiveFunction;
if (threads == 1) {
System.out.println("Computing model parameters ...");
objectiveFunction = new NegLogLikelihood(indexer);
} else {
System.out.println("Computing model parameters in " + threads + " threads ...");
objectiveFunction = new ParallelNegLogLikelihood(indexer, threads);
}
QNMinimizer minimizer = new QNMinimizer(
l1Cost, l2Cost, iterations, m, maxFctEval, printMessages);
minimizer.setEvaluator(new ModelEvaluator(indexer));
double[] parameters = minimizer.minimize(objectiveFunction);
// Construct model with trained parameters
String[] predLabels = indexer.getPredLabels();
int nPredLabels = predLabels.length;
String[] outcomeNames = indexer.getOutcomeLabels();
int nOutcomes = outcomeNames.length;
Context[] params = new Context[nPredLabels];
for (int ci = 0; ci < params.length; ci++) {
List outcomePattern = new ArrayList<>(nOutcomes);
List alpha = new ArrayList<>(nOutcomes);
for (int oi = 0; oi < nOutcomes; oi++) {
double val = parameters[oi * nPredLabels + ci];
outcomePattern.add(oi);
alpha.add(val);
}
params[ci] = new Context(ArrayMath.toIntArray(outcomePattern),
ArrayMath.toDoubleArray(alpha));
}
return new QNModel(params, predLabels, outcomeNames);
}
/**
* For measuring model's training accuracy
*/
private static class ModelEvaluator implements Evaluator {
private DataIndexer indexer;
public ModelEvaluator(DataIndexer indexer) {
this.indexer = indexer;
}
/**
* Evaluate the current model on training data set
* @return model's training accuracy
*/
@Override
public double evaluate(double[] parameters) {
int[][] contexts = indexer.getContexts();
float[][] values = indexer.getValues();
int[] nEventsSeen = indexer.getNumTimesEventsSeen();
int[] outcomeList = indexer.getOutcomeList();
int nOutcomes = indexer.getOutcomeLabels().length;
int nPredLabels = indexer.getPredLabels().length;
int nCorrect = 0;
int nTotalEvents = 0;
for (int ei = 0; ei < contexts.length; ei++) {
int[] context = contexts[ei];
float[] value = values == null ? null : values[ei];
double[] probs = new double[nOutcomes];
QNModel.eval(context, value, probs, nOutcomes, nPredLabels, parameters);
int outcome = ArrayMath.argmax(probs);
if (outcome == outcomeList[ei]) {
nCorrect += nEventsSeen[ei];
}
nTotalEvents += nEventsSeen[ei];
}
return (double) nCorrect / nTotalEvents;
}
}
}
